Hyderabad, Telangana, India
Information Technology
Full-Time
Bilvantis Technologies
Overview
Senior Data Modeler designs and optimizes enterprise data models and data warehouse architectures for analytics and reporting. They collaborate with engineers and analysts to ensure scalability, data integrity, and governance best practices. Expertise in dimensional modeling, SQL optimization, and cloud data warehouses is essential.
Experience Requirements
Data Modeling & Architecture:
Experience Requirements
- Bachelor’s degree in computer science, Information Systems, or a related field.
- 8+ years of experience in data modeling, data architecture, and enterprise DWH development.
- Strong expertise in at least one Columnar MPP Cloud Data Warehouse (Snowflake, Azure Synapse, Redshift).
- Experience with ETL/ELT tools (Azure Data Factory, DBT, Fivetran) to support data model implementation.
- Advanced SQL development skills, including query optimization, stored procedures, and indexing strategies.
- Hands-on experience in dimensional modeling, data vault modeling, and OLAP architectures.
- Experience working with data modeling tools (Erwin, DBT, SQL DB Modeler, or similar).
- Familiarity with data governance frameworks, data lineage tracking, and MDM solutions.
- Experience in agile development processes using Jira and Confluence.
- Knowledge of Python for data transformation and automation.
- Domain expertise in healthcare data (provider credentialing, claims processing, payer networks).
Data Modeling & Architecture:
- Design and develop conceptual, logical, and physical data models to support enterprise-wide DWH, data marts, and OLAP cubes for analytics and reporting.
- Implement dimensional modeling (star/snowflake schemas) for analytical workloads and normalized models (3NF) for operational data stores (ODS).
- Optimize data structures for performance, scalability, and maintainability, ensuring they support complex analytical queries.
- Create and maintain data dictionaries, metadata, and ER diagrams to document models effectively.
- Define slowly changing dimensions (SCD), surrogate keys, fact tables, and aggregates to enhance analytical reporting.
- Architect and implement Enterprise Data Warehousing (EDW) solutions to consolidate and centralize business data.
- Work with Data Engineers to develop ETL/ELT strategies for ingesting, transforming, and storing data efficiently in cloud DWH environments.
- Optimize data partitioning, clustering, indexing, and query performance tuning within Snowflake, Redshift, or Azure Synapse.
- Develop and maintain data pipelines to support real-time and batch data processing.
- Define data retention, archiving, and purging strategies to optimize storage costs.
- Establish data governance best practices to ensure accuracy, consistency, and security across all data assets.
- Implement data lineage tracking, cataloging, and MDM (Master Data Management) strategies to improve data discoverability.
- Work with compliance teams to enforce security, access control, and regulatory compliance (HIPAA, GDPR, SOC 2, etc.) in data management.
- Develop data quality frameworks to monitor anomalies, enforce validation rules, and handle missing or inconsistent data.
- Create and maintain comprehensive documentation, including data flow diagrams, transformation logic, mapping documents, and ETL specifications.
- Conduct data model reviews, knowledge-sharing sessions, and training for engineering and analytics teams.
- Stay updated with industry trends in DWH architectures, cloud data platforms, and data modeling tools.
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